Gray Level Local Dissimilarity Map and Global Dissimilarity Index for Quality of Medical Images

In order to evaluate performance quality of coding techniques, it is needed to have a good global index and a local index allowing the localisation of the distortions. In this study, a local dissimilarity map is presented for gray-level images. Its application to the comparison of a compressed image and its reference allows an excellent visual detection of the distortions. A global dissimilarity index is computed from the local dissimilarity map. These new measures are compared to the structural similarity index (SSIM). The results of the global measure are as good as the SSIM. The results of the local measure are quite superior to the SSIM computed in a local window. We claim these good results come from the consistency of the proposed index. It is more consistent to compute a global measure from a local one, than a local measure from a global one.